deepmipt/DeepPavlov
DeepPavlov
DeepPavlov is a deep learning conversational AI framework designed for building end-to-end dialog systems and chatbots. It functions as an NLP model training library and a pipeline system that connects multiple natural language processing models into a single operational chain.
The framework provides a REST API model server to expose trained deep learning models as web endpoints. This allows conversational agents to be deployed as web services that handle incoming HTTP requests and return predictions.
The system covers the full lifecycle of conversational AI development, including NLP pipeline orchestration, dataset-driven model training, and performance evaluation using specific scoring metrics. It supports both interactive and batch-mode inference to generate predictions from configured models.
Features
- Dialog System Frameworks - Provides a complete modular framework for building end-to-end chatbots and conversational agents.
- Modular Pipeline Orchestrators - Manages the flow of data through a sequence of modular language models to create conversational agents.
- Model Training Frameworks - Provides a library for training and evaluating NLP models using custom datasets and metrics.
- Niche NLP Model Training - Enables the training of specialized NLP models on custom datasets via scripts or a command line interface.
- Model Pipelines - Chains natural language processing models into a single operational pipeline for chatbot deployment.
- Model Serving - Provides the infrastructure to deploy trained conversational agents as RESTful web services for remote inference.
- Dataset-Driven Training - Allows training deep learning models using labeled conversational data through predefined loaders and a CLI.
- Conversational AI - Provides a platform for building end-to-end chatbots and dialogue systems using NLP components.
- Configuration-Driven Assembly - Connects independent NLP components into a sequential chain via structured configuration files.
- Conversational AI Frameworks - Functions as a deep learning framework specifically for building end-to-end dialog systems and chatbots.
- Batch Inference Pipelines - Provides a system for processing large sets of input text through a model pipeline in non-interactive batch mode.
- Chatbot Hosting Services - Provides network APIs for hosting and distributing access to trained conversational models.
- Agent Performance Evaluators - Implements tools for assessing the performance and accuracy of dialog systems using specific datasets.
- Model Inference - Implements a system for generating predictions from models in both interactive and batch modes.
- Model Performance Evaluators - Quantifies model accuracy and quality by comparing predictions against ground truth labels in datasets.
- Evaluation Metrics - Ships tools to measure the accuracy and quality of generated responses against gold-standard datasets.
- Model API Servers - Serves as a backend model server that exposes trained deep learning models as web endpoints.
- Model Abstractions - Implements uniform interfaces to wrap complex deep learning architectures for consistent training and inference.
- REST APIs - Exposes trained models as web services for handling prediction requests over HTTP.
- Chatbots and QA - Library for building end-to-end dialogue systems.
- Natural Language Processing - Conversational AI library with pre-trained Russian models.
